Simona Lo Giudice
Applying Semantic Text Similarity to support the Identification of Human Factors in Aviation Accidents.
Rel. Guido Perboli, Stefano Musso. Politecnico di Torino, Master of science program in Engineering And Management, 2018
Abstract
In Aviation Safety Management, the final goal of the investigator, when approaching an accident, is to identify which are those factors that have realistically contributed to causing the unfortunate events leading to the accident. The aim of investigations is to draft, starting from the causes of the accident, safety recommendations and remedial actions that can eliminate avoidable human, economic and social costs. Previous work was conducted to formally describe how the analysis of an aircraft incident/accident could be leaded in a partially automatic way, developing a supporting expert system that can address the safety investigator during his inquiry. Currently, the system is able to process accident reports and classify the events following a particular safety standard.
Purpose of this thesis is to improve the current solution, introducing an intelligent system based on Machine Learning, which, starting from the classification of events, is able to identify in the text the factors that lead to the occurrence of an accident
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